Method and system for diagnostics and monitoring of electric machines

ABSTRACT

A system for use with an electric machine is provided. The system includes a processor and a memory comprising a set of memory modules, which, when executed by the processor, cause the processor to perform certain operations. The operations include receiving operational data from the electric machine, and generating, based on the operational data, a first set of diagnostic data, by executing a first memory module from the set of memory modules. The operations further include generating, based on the operational data, a second set of diagnostic data, by executing a second memory module from the set of memory modules, the second memory module including a set of parameters associated with a diagnostics model of the electric machine. Furthermore, the operations include effecting, based on the operational data, the first set of diagnostic data, and the second set of diagnostic data, a change in at least one parameter.

TECHNICAL FIELD

The present disclosure relates to electric machines. More particularly,the present disclosure relates to a method and a system for diagnosticsand monitoring of an electric machine.

BACKGROUND

Many traditional diagnostics and monitoring platforms for electricmachines use a two-step process to identify fault conditions. Forexample, typical diagnostics and monitoring platforms first use physicsmodel-based or other non-learning routines to flag data believed to showa fault condition. And, in a subsequent step, an expert technicianperforms a more thorough analysis using any combination of additionalanalytical tools and expert experience to evaluate the data to determinewhether the data shows either a properly diagnosed fault condition or afalse positive condition, before enunciating the fault condition.

These additional analytical steps add to the time between when a faultoccurs and when the fault condition is enunciated to the customer. Assuch, typical diagnostics and monitoring platforms add complexity todiagnostics and monitoring operations, and they require expert-leveltechnician input in order to properly analyze fault data, bothconditions that may cause unwanted down time and loss of revenue.

SUMMARY

The embodiments featured herein help solve or mitigate several theaforementioned issues. For example, the embodiments feature the use ofadaptive routines along with non-adaptive routines. The adaptiveroutines may include a neural network, a machine learning-based routine,or generally, a process that can have its operational parametersmodified or tuned using operational data. The non-adaptive routines maybe physics-based, finite element analysis (FEA)-based, or based on othernon-learning or other non-adaptive routines. In some embodiments, anexpert technician's assessments of the incoming data are aggregated sothat, through supervised learning, the adaptive diagnostics andmonitoring routine can be trained to identify false positives in theplace of the expert technician. Thus, the embodiments reduce systemcomplexity, and they allow a more rapid enunciation of a fault conditionin one or more electric machines. Consequently, the embodiments helpreduce or eliminate the demand on expert technician input to analyze thefault data. In addition, the use of non-adaptive methods with theadaptive methods helps mitigate or eliminate “learning” time across anentire system.

While a typical adaptive routine would require an extensive “learning”period in which the normal operation of the system is characterized andno fault identification or enumeration would be performed, in some ofthe embodiments, the use of a non-adaptive method for faultidentification in parallel with an adaptive method allows for faultidentification and enumeration even during the “learning” period of thesystem. Further, since typical adaptive routines assume that the systemis “healthy” during the learning period, the characterization of thesystem built by the adaptive routine may be incorrect; a non-adaptiveroutine, which is typically built from an ideal model of the system,would be able to detect faults even on a recently installed system.

One exemplary embodiment having the above-mentioned features andadvantages is a system that includes a processor and a memory comprisinga set of memory modules, which, when executed by the processor, causethe processor to perform certain operations. The operations includereceiving operational data from an electric machine, and generating,based on the operational data, a first set of diagnostic data, byexecuting a first memory module from the set of memory modules. Theoperations further include generating, based on the operational data, asecond set of diagnostic data, by executing a second memory module fromthe set of memory modules, the second memory module including a set ofparameters associated with a diagnostics model of the electric machine.Furthermore, the operations include effecting, based on the operationaldata, the first set of diagnostic data, and the second set of diagnosticdata, a change in at least one parameter.

Another embodiment provides a system for use with a set of electricmachines. The system includes a processor and a memory that includes aset of memory modules, which, when executed by the processor, cause theprocessor to perform certain operations. The operations can includereceiving operational data from at least two electric machines from theset of electric machines, and generating, based on the operational datafrom each of the at least two electric machines, first sets ofdiagnostic data, by executing a first memory module from the set ofmemory modules.

The operations can further include generating, based on the operationaldata from each of the at least two electric machines, second sets ofdiagnostic data, by executing a second memory module from the set ofmemory modules, the second memory module including a set of parametersassociated with a diagnostics model for the set of electric machines.Furthermore, the operations may include effecting, based on the firstsets of diagnostic data, the second sets of diagnostic data, and theoperational data from one of the at least two electric machines, achange in at least one parameter associated with the one of the at leasttwo electric machines.

Another embodiment provides a method for use with an electric machine.The method includes receiving, by a diagnostic unit, operational datafrom the electric machine. The method further includes generating, bythe diagnostic unit and based on the operational data, a first set ofdiagnostic data. The method further includes generating, by thediagnostic unit and based on the operational data, a second set ofdiagnostic data. The method further includes effecting, based on theoperational data, the first set of diagnostic data, and the second setof diagnostic data, a change in at least one parameter of a diagnosticmodel of the diagnostic unit used to generate the second set ofdiagnostic data.

Additional features, modes of operations, advantages, and other aspectsof various embodiments are described below with reference to theaccompanying drawings. It is noted that the present disclosure is notlimited to the specific embodiments described herein. These embodimentsare presented for illustrative purposes. Additional embodiments, ormodifications of the embodiments disclosed, will be readily apparent topersons skilled in the relevant art(s) based on the teachings provided.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments may take form in various components andarrangements of components. Illustrative embodiments are shown in theaccompanying drawings, throughout which like reference numerals mayindicate corresponding or similar parts in the various drawings. Thedrawings are for purposes of illustrating the embodiments and are not tobe construed as limiting the disclosure. Given the following enablingdescription of the drawings, the novel aspects of the present disclosureshould become evident to a person of ordinary skill in the relevantart(s).

FIG. 1 illustrates a system in accordance with several aspects describedherein.

FIG. 2 illustrates a computational unit in accordance with severalaspects described herein.

FIG. 3 illustrates a block diagram of a controller in accordance withseveral aspects described herein.

FIG. 4A illustrates a system in accordance with several aspectsdescribed herein.

FIG. 4B illustrates a system in accordance with several aspectsdescribed herein.

FIG. 5 depicts a flow chart of a routine in accordance with severalaspects described herein.

FIG. 6 depicts a flow chart of a routine in accordance with severalaspects of the subject matter in accordance with one embodiment.

DETAILED DESCRIPTION

While the illustrative embodiments are described herein for particularapplications, it should be understood that the present disclosure is notlimited thereto. Those skilled in the art and with access to theteachings provided herein will recognize additional applications,modifications, and embodiments within the scope thereof and additionalfields in which the present disclosure would be of significant utility.

Some embodiments include a remote diagnostics and monitoring system andassociated method of operation of the system. In the exemplary system, aprocessor executes a physics-based, finite element analysis (FEA)-based,or other non-learning or generally, other non-adaptive routine, toproduce a first set of outputs. The first set of outputs is combinedwith a second set of outputs obtained from an adaptive routine, alsoexecuted by the processor. The adaptive routine may feature a neuralnetwork, a machine learning-based routine, or generally, a process thatcan have its operational parameters modified or tuned using operationaldata. A routine may be construed herein as a program configured in wholeor in part to cause a processor to perform certain operations.

Both the adaptive and the non-adaptive routines produce the same healthstatus indicator of an electric machine (e.g., both the non-adaptive andthe adaptive routines can determine if a broken rotor bar is present inthe electric machine). In the embodiments, combining the first set ofoutputs, the second set of outputs, and data obtained based on theassessment of the expert technician enables supervised learning in thesystem, which reduces or eliminates required expert technicianintervention in addition to reducing the number of false positivesand/or false negatives in the system.

The system includes an electric machine and a diagnostic unit thatincludes a computational unit (comprising a processor and memory)configured to receive operational data (at least one of voltage,current, vibration signature, temperature, and insulation integrity) ofone or more electric machines and perform, on the operational data, atleast one diagnostic test. The at least one diagnostic test can beexecuted by either one or both of a non-adaptive routine and an adaptiveroutine stored in the memory of a processor.

The embodiments further include a supervisory routine also stored in thememory and which coordinates the outputs of the non-adaptive and theadaptive routines. In one embodiment, an exemplary system has a“learning” phase in which the supervisory routine sends the results ofboth the non-adaptive routine and the adaptive routine to an expertmachine technician who can then classify whether the output constitutesa true fault or a false positive. Further, the expert can update or tunethe weights of the adaptive routine using this new data inputclassification. In another embodiment, a “trained” phase of the systemrequires no technician intervention, and the supervisory routinedetermines if there is a fault in the system using any combination ofthe non-adaptive and adaptive routines.

In another embodiment, the system has a “semi-trained” phase in which notechnician intervention is required if the supervisory routinedetermines that the outputs of the non-adaptive and adaptive routinesboth show identical results (within a margin of error) but in which thesupervisory routine will send the results of both the non-adaptiveroutine and the adaptive routine to the expert machine technician forfurther assessment if the routines' results deviate from each other(e.g., beyond a predetermined acceptable margin of error). Severalembodiments consistent with the aforementioned features and advantagesare described below in regards to FIGS. 1-5.

FIG. 1 illustrates a system 100 according to an embodiment. The system100 includes an electric machine 102 that is powered by an electricsupply 112. The electric supply 112 may drive the electric machine 102by providing it current and voltage, in addition to other controlcommands, via a plurality of wires that form a power bus 118. Thecurrents and voltages are respectively measured by a current sensor 124and a voltage sensor 120. The electric supply 112 may be comprised of apower converter, including, without limitation, a variable frequencydrive (VFD), which may, through a plurality of control routines orregimen stored in a memory of the power converter controller, determinethe voltage and current supplied to power bus 118.

The system 100 further includes a diagnostics unit 104 that is coupledwith the electric machine 102 and that may serve to obtain status dataor status information from the electric machine 102. For example, thediagnostics unit 104 can be configured to retrieve or to instruct theelectric machine 102 to send a set of sensor measurements 114, which mayinclude, for example, and not by limitation, temperature, voltage,current, vibration, and insulation integrity data pertaining to theelectric machine 102. The sensor measurements 114 may be construed asoperational data that pertain to a state of the electric machine 102.

The diagnostics unit 104 includes a computational unit 106 that caninclude a computational unit, which can be a processor configured toexecute at least one monitoring and diagnostics routine, as shall bedescribed in further details below. As such, the diagnostics unit 104may perform one or more diagnostics tests based on the operational data.For example, and not by limitation, the diagnostics unit 104 may infer astate of the electric machine 102 based on at least one of the voltage,current, vibration signature, temperature, and insulation integrity,which may be provided to the diagnostics unit 104 as part of the sensormeasurements 114.

The computational unit 104 may output data to a terminal 130 accessibleto one or more expert technicians via an interface link 132. As shall bedescribed in further details below, the terminal 130 may alsocommunicate with the computational unit 104 to provide it updates forone or more adaptive routines.

It should be understood that while the block diagram 100 features theinterconnection between the diagnostics unit 104 with the terminal 130and the electric machine 102, various implementations may be achievedwithout departing from the scope of the disclosure. For example, thediagnostics unit 104 may be a module that is part of the electricmachine 102, or in alternate implementations, part of the terminal 130.

FIG. 2 depicts a block diagram 300 of the computational unit 106contained within the diagnostics unit 104. Generally, the computationalunit 106 may be a core, processor, or an embedded application-specificcomputer that is programmed by instructions, i.e., routines, stored in amemory. The instructions configure the processor to perform severaloperations that cause the diagnostics unit to perform as featuredthroughout the present disclosure.

The computational unit 106 includes a first computational module 302, asecond computational module 304, and a third computational module 306.The computational unit 106 receives operational data, e.g., the sensormeasurements 114, and the operational data is processed through theexecution of instructions stored in the first computational module 302and in the second computational module 304. The first computationalmodule 302 may contain a non-adaptive diagnostics routine whereas thesecond computational module 304 may contain an adaptive diagnosticsroutine. The execution of these routines results in non-adaptive routinediagnostic output 308 and adaptive routine diagnostic output 310,respectively.

As configured, the diagnostics unit 104 performs a diagnostic analysison adaptive routine diagnostic output 310 the asset (the electricmachine 102), using both the non-adaptive routine and the adaptiveroutine (e.g., neural network, machine learning-based routine, or otherroutine which can have its operational parameters modified byoperational data).

The third computational module 306, which includes a supervisory routineor module that determines whether the operational data and the routineoutputs (308 and 310) should be sent to the terminal 130 for furtheranalysis.

In one example, an expert technician, using any combination of expertexperience or additional computational techniques, may perform ananalysis of the received data 314, which may consist of at least one ofthe non-adaptive routine diagnostic output 308, the adaptive routinediagnostic output 310, and the operational data, to evaluate whether thereceived data 314 shows a fault condition or does not show a faultcondition. The received data 314 may then be added to a database, alongwith a classification of the data from the expert technician. If theexpert technician determined a fault condition was present, a faultcondition may be enunciated, i.e., the electric machine 102 may bemarked for repair.

The expert technician can then choose to retrain (calculate newparameters/inputs for) the adaptive routine in the second memory module304 in order to tune the performance of the adaptive routine andactivate these changes to the adaptive routine by transmitting a set ofcommands and the necessary changes to the computational unit 106 via theinterface 316 between the terminal and the computational unit.

In contrast, if the expert technician determines, through anycombination of expert experience and computational techniques, that theadaptive routine has been tuned to a performance deemed acceptable, thetechnician can choose to modify the supervisory routine of the thirdcomputational module 306. This modification determines whether theoperational data and the routine outputs (308 and 310) should be sent tothe expert technician for additional analysis, via the interface 316between the terminal and the computational unit 106. Further, thetechnician can choose to allow any combination of routine outputs todirectly enunciate the fault condition without requiring prior experttechnician assessment to confirm the fault condition. In the lattercase, no expert technician intervention is needed for subsequentdetections of faults.

As such, the embodiments provide a system that reduces expert technicianintervention and that reduces false positives and false negatives in adiagnostics and monitoring platform. A supervisory routine (e.g., in thecomputational module 306), using the outputs of the non-adaptive andadaptive routines (308 and 310, respectively) as well as the operationaldata input from sensor measurements 114, is used to determine whetherexpert technician intervention is required (e.g., to confirm a faultcondition identified by the adaptive and the non-adaptive routines).

The expert technician may use collected data, as well as the outputs ofthe non-adaptive and adaptive routines to then tune the adaptiveroutine's performance. If the adaptive routine performance is deemed bythe expert technician to be acceptable, then the expert technician canchange a rules-based system used to determine whether technicianintervention is required to reduce or eliminate notifications to theexpert technician based on the performance of the routines. Furtherdescription of an example use of the system 100 will be described inregards to FIG. 5.

FIG. 3 shows a block diagram of a controller 400, representing asoftware/firmware and hardware implementation of diagnostics unit 104.The controller 400 includes a processor 402 that has a specificstructure. The specific structure is imparted to the processor 402 byinstructions stored in a memory 404 and/or by instructions 420 that maybe fetched by the processor 402 from a storage medium 418. The storagemedium 418 may be co-located with the controller 400 as shown, or it maybe located elsewhere and be coupled to the controller 400. Further, thecontroller 400 can be a stand-alone programmable system, or it can be aprogrammable module located in a much larger system. For example, thecontroller 400 may be integrated with the electric machine 102 or withthe terminal 130.

The controller 400 may include one or more hardware and/or softwarecomponents configured to fetch, decode, execute, store, analyze,distribute, evaluate, and/or categorize information. Furthermore,controller 400 can include an input/output (I/O) 414 that is configuredto interface with an electric machine 102, or with a plurality ofelectric machines like the electric machine 102.

The processor 402 may include one or more processing devices or cores(not shown). In some embodiments, the processor 402 may be a pluralityof processors, each having either one or more cores. The processor 402can be configured to execute instructions fetched from the memory 404,i.e. from one of the memory block 412, the memory block 410, the memoryblock 408, or the memory block 406, or the instructions may be fetchedfrom the storage medium 418, or from a remote device connected to thecontroller 400 via a communication interface 416.

Furthermore, without loss of generality, the storage medium 418 and/orthe memory 404 may include a volatile or non-volatile, magnetic,semiconductor, tape, optical, removable, non-removable, read-only,random-access, or any type of non-transitory computer-readable computermedium. The storage medium 418 and/or the memory 404 may includeprograms and/or other information that may be used by the processor 402.Furthermore, the storage medium 418 may be configured to log dataprocessed, recorded, or collected during the operation of controller400. The data may be time-stamped, location-stamped, cataloged, indexed,or organized in a variety of ways consistent with data storage practice.

The memory block 406 may include a first adaptive routine, the memoryblock 408 may include a second adaptive routine, the memory block 410may include a non-adaptive routine, and the memory block 412 may includea supervisory routine. In one embodiment, the processor 402 may beconfigured by instructions in the memory 404 to perform certainoperations.

The operations can include receiving operational data from the electricmachine 102. The operations further include generating a first set ofdiagnostic data by the processor 402 executing the non-adaptive moduleof the memory block 410. The operations further include generating asecond set of diagnostic data by the processor 402 executing theadaptive module of either one of the memory blocks 406 or 408. Theadaptive module may include an adaptive routine that includes a set ofparameters associated with a diagnostics model of the electric machine.The operations may further include effecting, based on the operationaldata, the first set of diagnostic data, and the second set of diagnosticdata, a change in at least one parameter in the adaptive routine.

In another embodiment, the controller 400 may serve as hub that receivesoperational data from a plurality of electric machines. In this case,the processor 402 can perform operations that include receivingoperational data from at least two electric machines from a set ofelectric machines. The operations can include generating, based on theoperational data from each of the at least two electric machines, firstsets of diagnostic data, by executing a first memory module from thatincludes a non-adaptive routine (e.g., the memory module 410).

The operations may further include generating, based on the operationaldata from each of the at least two electric machines, second sets ofdiagnostic data, by executing an adaptive routine (e.g., the memorymodule 406). The adaptive routine can include a set of parametersassociated with a diagnostics model for the set of electric machines.Moreover, the operations can include effecting, based on the first setsof diagnostic data, the second sets of diagnostic data, and theoperational data from one of the at least two electric machines, achange in at least one parameter of the adaptive routine.

FIG. 4A illustrates a system 430 in accordance with an embodiment. Thesystem 430 includes two electric machines denoted M1 and M2. Eachelectric machine is associated with a specific diagnostic unit. Forexample, the electric machine M1 is associated with a diagnostic unit421, and the electric machine M2 is associated with a diagnostic unit422. Each diagnostic unit may be incorporated within its correspondingelectric machine. Generally, each diagnostic unit may be co-located withits corresponding electric machine. Further, while only two electricmachines are shown, the system 430 may include more than two electricmachines, and each electric machine may have its own diagnostic unit.

The diagnostic unit 421 may be a controller similar to the controller400 described above. Specifically, the diagnostic unit 421 may include aprocessor 421 a and a memory that includes a first module 421 b and asecond module 421 c. The first module 421 b may include instructionsconsistent with a non-adaptive routine that cause the processor 421 a togenerate a first set of diagnostic data based on operational datareceived by the diagnostic unit 421. The second module 421 c may includeinstructions that cause the processor 421 a to generate a second set ofdiagnostic data based on the received operational data. Furthermore, thesecond module 421 c may include a set of parameters that configure theprocessor 421 a as part of an adaptive routine that generates the secondset of diagnostic data.

The diagnostic unit 422, which is associated with the electric machineM2, may be configured to function with respect to the electric machineM2 in a manner similar to the diagnostic unit 421. Specifically, theprocessor 422 a may be configured to generate, based on receivedoperational data from the electric machine M2, a first set of diagnosticdata based on a non-adaptive routine of the first module 422 b and asecond set of diagnostic data based on an adaptive routine stored in thesecond module 422 c. Furthermore, the second module 422 c may include aset of parameters that define the adaptive routine stored in the secondmodule 422 c.

In one use case, the diagnostic unit 421 may communicate the first setof diagnostic data, the operational data, and the second set ofdiagnostic data of the electric machine M1 to the diagnostic unit 422.The processor 422 a may effect a change in the set of parametersincluded in the second module 422 c based on the data received from thediagnostic unit 421. In other words, in the system 430, diagnostic datafrom one electric machine may be used to effect a change in the set ofparameters of an adaptive routine associated with another electricmachine. Generally, diagnostic data from one electric machine may beused to effect a change in the adaptive control routines of more thanone other electric machine in the system.

FIG. 4B illustrates a system 432 in accordance with another embodiment.The system 432 is configured similarly to the system 430, with theexception that a remote terminal 423 may receive the data from each ofthe diagnostic units 421 and 422. The remote terminal 423 may beaccessible by an expert operator who may then effect a change in a setof parameters in a particular diagnostics unit (e.g., the set ofparameters included in the second module 422 c of the diagnostics unit422) from data received from the diagnostics unit 421.

Having set forth several embodiments, methods 500 and 600, which areconsistent with their operation, is now described with respect to FIG. 5and FIG. 6, respectively. FIG. 5 illustrates the method 500, which canbe executed by the diagnostics unit 104. The method 500 begins at step502, and it includes receiving (at step 502) operational data from anelectric machine. The method 500 includes generating (at step 504),based on the operational data, a first set of diagnostic data.

The method 500 further includes generating (at step 506), based on theoperational data, a second set of diagnostic data. The first set ofdiagnostic data may be generated according to a non-adaptive routine andthe second set of diagnostic data may be generated according to anadaptive routine. The method 500 further includes effecting (at step508), based on the operational data, the first set of diagnostic data,and the second set of diagnostic data (step 510), a change in at leastone parameter of a diagnostic model. The diagnostic model is associatedwith the adaptive routine used to generate the second set of diagnosticdata. The process ends at step 512.

FIG. 6 depicts a flow chart of the method 600, which is yet anotherexemplary operation of the systems described herein. The method 600 maybegin at step 602, and it includes acquiring/receiving operational databy a diagnostics unit (step 604). The operational data may then beanalyzed using both non-adaptive and adaptive routines (step 602). Adetermination may be made at decision block 608, by the diagnostic unit,whether the system is placed in a learning phase, a setting that maydepend on previously received commands by the diagnostics unit. If thedetermination is negative, the method 500 moves to the decision block610, wherein the diagnostics unit is in a self-learning phase, which isagain, a state that may be determined by the diagnostics unit based onpreviously received information.

If the determination at the decision block 610 is negative, the method600 moves to the decision block 612, at which point the method 600places the diagnostics unit in a “trained” phase. At decision block 612,a remote terminal (e.g., the terminal 204) determines whether acombination of machine-learning routines (i.e., adaptive routines) andnon-adaptive routines identify a fault. If no, the method 600 returns tothe step 604. If a fault is detected, then the fault is enunciated (step614), before the method 600 moves to decision block 616.

At block 616, it is determined, e.g., by the supervisory routineassociated with the computational module 306, whether results from theadaptive and non-adaptive routines deviate from past thresholds. If theresults do not deviate, the method 600 returns to the step 604.Otherwise, the method continues to step 618, where the results areprovided to an expert operator for examination.

Upon examination, the operator determines whether to change theoperational phase of the system back to a learning phase (step 620), andshe further decides to either remain in the trained phase, to return tothe learning phase, or to enter into the self-training phase. The methodthen moves back to the step 604.

At step 610, if the determination is made at the decision block 610 thatthe system is in a self-learning phase, the method 600 moves to thedecision block 622, wherein it is determined (e.g., by the supervisoryroutine associated with the computational module 306), whether acombination of machine-learning routines (i.e., adaptive routines) andnon-adaptive routines identifies a fault. If a fault is identified, thenit is enunciated (step 624) and the method 600 moves to step 626 wherethe adaptive routines are retrained using any combinations of new data,the results of the non-adaptive routine, and that of the adaptiveroutine. Similarly, if a fault is not detected at the decision block622, the method 600 moves to the step 626.

After retraining, the method 600 moves to the decision block 628,wherein it is determined, e.g., by the supervisory routine associatedwith the computational module 306, whether results from the adaptive andnon-adaptive routines deviate from past thresholds. If the determinationis negative, the method 600 moves back to the step 604. Otherwise, themethod continues to step 630, where the results are provided to anexpert operator for examination. Upon examination, the operatordetermines whether to change the operational phase of the system back toa learning phase (step 632), and she further decides to either remain inthe self-learning phase, to return to the learning phase, or to enterinto the trained phase. The method then moves back to the step 604.

At the decision block 608, when the determination is positive, i.e.,when the system is in a learning phase, the method 600 moves to thedecision block 634, where it is determined whether a non-adaptiveroutine has identified a fault condition. If not, the method 600 returnsto the step 604. Otherwise, the method 600 continues to the step 636,where the results of the non-adaptive routine, as well as the results ofthe adaptive routines, are sent to the expert operator along with theoperational data acquired at step 604.

The method 600 then moves to the decision block 638 to determine whethera false positive was identified by the non-adaptive routine. If yes, andno fault occurred, and the method moves to the step 642. If no, and thefault was correctly identified, then the fault is enunciated (step 640),and the method 600 also moves to the step 642, at which point theadaptive routine is retrained using any combination of new data,existing data, results from the non-adaptive routine, results from theadaptive routine, and operator fault classification information (thedetermination of whether a fault was a false positive or a truepositive).

From the step 642, the method 600 then moves to the decision block 644,where an expert operator determines whether the adaptive routine isfully trained. If such a determination is negative, the method 600returns to the step 604. Otherwise, the method 600 moves to the step 646where the expert can move the system phase to “trained” or“self-learning.” The method 600 then returns to the step 604. The method600 can then continually run; as can readily be understood by one ofskill in the art and from the flow chart of the method 600, the more theadaptive routine is trained, the less expert operator intervention isrequired when in the self-learning or trained phases. Specifically, whenproperly trained, the system may move from the step 604 to the decisionblock 616 and return back to the step 604, thus eliminating the operatorintervention.

Generally, the embodiments leverage an adaptive routine to reduce thenumber of false positives in a system. As such, the embodiments allowmore accurate results than a generalized non-adaptive routine wouldprovide. Further, with the embodiments, the need for expert technicianintervention is greatly reduced. When a fleet of similar assets aremonitored, the data from all of these assets can be used to furtherrefine the adaptive routine's performance.

The embodiments allow several technical and commercial advantages. Forexample, the embodiments help reduce the need for expert technicianintervention, which increases response time if an asset fault isdiagnosed. Further, the embodiments allow a reduction in falsepositives, which correlates with costs savings since fewer personnelneed to be involved in classifying diagnostics and monitoring data.

Several embodiments consistent with the teachings presented herein aredescribed below. These embodiments are examples and should not beconstrued as limiting the disclosure. Further, one of skill in the artwill readily recognize that several modifications and adaptations of theembodiments described below can be achieved without departing from thescope of the present disclosure.

One embodiment provides a system for use with an electric machine. Thesystem includes a processor and a memory comprising a set of memorymodules, which, when executed by the processor, cause the processor toperform certain operations. The operations include receiving operationaldata from the electric machine, and generating, based on the operationaldata, a first set of diagnostic data, by executing a first memory modulefrom the set of memory modules. The operations further includegenerating, based on the operational data, a second set of diagnosticdata, by executing a second memory module from the set of memorymodules, the second memory module including a set of parametersassociated with a diagnostics model of the electric machine.Furthermore, the operations include effecting, based on the operationaldata, the first set of diagnostic data, and the second set of diagnosticdata, a change in at least one parameter.

The operational data may include sensor data received by the processorfrom one or more sensors associated with the electric machine. In onescenario, a technician may, based on the operational data, the firstdata set, and the second data set, generate user data and communicatesuch user data to the processor. The user data may include a set ofcommands that instruct the processor to alter one or more diagnosticsparameters represented in the second memory module. As such, the secondmemory module may function as a set of adaptive routines that can beupdated based on a previous execution (by the processor) of the firstmemory module, the second memory module, and user supplied data.

In other words, the second memory module is configured to perform anadaptive process on the operational data. In contrast, the first memorymodule is configured to perform a non-adaptive process on theoperational data. In the case of the first memory module, thenon-adaptive process may be a physics-based model of the electricmachine or a finite-element-analysis (FEA)-based model of the electricmachine. Generally, the first memory module is based anon-learning-based routine whereas the second memory module pertains toa learning-based routine.

One embodiment provides a system for use with a set of electricmachines. The system includes a processor and a memory that includes aset of memory modules, which, when executed by the processor, cause theprocessor to perform certain operations. The operations can includereceiving operational data from at least two electric machines from theset of electric machines, and generating, based on the operational datafrom each of the at least two electric machines, first sets ofdiagnostic data, by executing a first memory module from the set ofmemory modules.

The operations can further include generating, based on the operationaldata from each of the at least two electric machines, second sets ofdiagnostic data, by executing a second memory module from the set ofmemory modules, the second memory module including a set of parametersassociated with a diagnostics model for the set of electric machines.Furthermore, the operations may include effecting, based on the firstsets of diagnostic data, the second sets of diagnostic data, and theoperational data from one of the at least two electric machines, achange in at least one parameter associated with the one of the at leasttwo electric machines.

Another embodiment provides a method for use with an electric machine.The method includes receiving, by a diagnostic unit, operational datafrom the electric machine. The method further includes generating, bythe diagnostic unit and based on the operational data, a first set ofdiagnostic data. The method further includes generating, by thediagnostic unit and based on the operational data, a second set ofdiagnostic data. The method further includes effecting, based on theoperational data, the first set of diagnostic data, and the second setof diagnostic data, a change in at least one parameter of a diagnosticmodel of the diagnostic unit used to generate the second set ofdiagnostic data.

Furthermore, yet another embodiment may be a system that does notinclude or use a non-adaptive routine. In other words, the first andsecond memory modules mentioned above may each cause the processor toexecute two adaptive routines. Each of the adaptive routines can then betrained until they reach such a point that user intervention is notrequired or is infrequently required. Sated otherwise, in for thisalternate embodiment, the non-adaptive routine described in the method600 may be replaced by another adaptive routine, and training mayrequire altering both adaptive routines.

Another embodiment may be a system for use with a first electric machineand a second electric machine. The system includes a processor and amemory including a set of memory modules, which when executed by theprocessor, cause the processor to perform certain operations. Theoperations can include receiving operational data from a diagnosticsunit of the first electric machine and generating, based on theoperational data, a first set of diagnostic data, by executing a firstmemory module from the set of memory modules.

The operations can further include generating, based on the operationaldata, a second set of diagnostic data, by executing a second memorymodule from the set of memory modules, the second memory moduleincluding a set of parameters associated with a diagnostics model forthe first electric machine. Furthermore, the operations can includeeffecting, based on the first set of diagnostic data, the second set ofdiagnostic data, and the operational data, a change in at least oneparameter associated with the second electric machine.

Those skilled in the relevant art(s) will appreciate that variousadaptations and modifications of the embodiments described above can beconfigured without departing from the scope and spirit of thedisclosure. Therefore, it is to be understood that, within the scope ofthe appended claims, the teachings set forth in the present disclosuremay be practiced other than as specifically described herein.

What is claimed is:
 1. A system for use with an electric machine,comprising: a processor; a memory comprising a set of memory modules,which when executed by the processor, cause the processor to performoperations including: receiving operational data from the electricmachine; generating, based on the operational data, a first set ofdiagnostic data, by executing a first memory module from the set ofmemory modules; generating, based on the operational data, a second setof diagnostic data, by executing a second memory module from the set ofmemory modules, the second memory module including a set of parametersassociated with a diagnostics model of the electric machine; andeffecting, based on the operational data, the first set of diagnosticdata, and the second set of diagnostic data, a change in at least oneparameter from the set of parameters.
 2. The system of claim 1, whereinthe operational data includes sensor data associated with the electricmachine, and wherein the sensor data includes at least one of voltage,current, temperature, vibration signature, and insulation integrity ofthe electric machine.
 3. The system of claim 1, wherein the effecting isfurther based on data received by the processor from a remote terminal,the data being associated with an assessment from an operator.
 4. Thesystem of claim 1, wherein the second memory module is configured tocause the processor to perform an adaptive processing of the operationaldata.
 5. The system of claim 1, wherein the first memory module isconfigured to cause the processor to perform a non-adaptive processingof the operational data.
 6. The system of claim 5, wherein thenon-adaptive processing is based on a physics-based model of theelectric machine.
 7. The system of claim 5, wherein the non-adaptiveprocessing is based on a finite-element-analysis (FEA)-based model ofthe electric machine.
 8. The system of claim 5, wherein the non-adaptiveprocessing is based on a non-learning-based model of the electricmachine.
 9. A system for use with a set of electric machines, the systemcomprising: a processor; a memory comprising a set of memory modules,which when executed by the processor, cause the processor to performoperations including: receiving operational data from at least twoelectric machines from the set of electric machines; generating, basedon the operational data from each of the at least two electric machines,first sets of diagnostic data, by executing a first memory module fromthe set of memory modules; generating, based on the operational datafrom each of the at least two electric machines, second sets ofdiagnostic data, by executing a second memory module from the set ofmemory modules, the second memory module including a set of parametersassociated with a diagnostics model for the set of electric machines;and effecting, based on the first sets of diagnostic data, the secondsets of diagnostic data, and the operational data from one of the atleast two electric machines, a change in at least one parameterassociated with the one of the at least two electric machines.
 10. Thesystem of claim 9, wherein the operational data includes sensor data.11. The system of claim 9, wherein the effecting is further based ondata received by the processor, the data being associated with anassessment from an operator.
 12. The system of claim 9, wherein thesecond memory module is configured to cause the processor to perform anadaptive processing of the operational data.
 13. The system of claim 9,wherein the first memory module is configured to cause the processor toperform a non-adaptive processing of the operational data.
 14. A methodfor use with an electric machine, the method comprising: receiving, by adiagnostic unit, operational data from the electric machine; generating,by the diagnostic unit and based on the operational data, a first set ofdiagnostic data; generating, by the diagnostic unit and based on theoperational data, a second set of diagnostic data; and effecting, basedon the operational data, the first set of diagnostic data, and thesecond set of diagnostic data, a change in at least one parameter of adiagnostic model of the diagnostic unit used to generate the second setof diagnostic data.
 15. The method of claim 14, wherein the operationaldata includes sensor data associated with the electric machine.
 16. Themethod of claim 14, wherein the effecting is further based on datareceived by the diagnostic unit from a remote terminal, the data beingassociated with an assessment from an operator.
 17. The method of claim14, wherein the second set of diagnostic data is generated according toan adaptive model.
 18. The method of claim 14, wherein the first set ofdiagnostic data is generated according to a non-adaptive model.
 19. Themethod of claim 18, wherein the non-adaptive model is a physics-basedmodel of the electric machine.
 20. The method of claim 18, wherein thenon-adaptive model is a finite element analysis (FEA)-based model of theelectric machine.
 21. A system for use with a first electric machine anda second electric machine, the system comprising: a processor; a memorycomprising a set of memory modules, which when executed by theprocessor, cause the processor to perform operations including:receiving operational data from a diagnostics unit of the first electricmachine; generating, based on the operational data, a first set ofdiagnostic data, by executing a first memory module from the set ofmemory modules; generating, based on the operational data, a second setof diagnostic data, by executing a second memory module from the set ofmemory modules, the second memory module including a set of parametersassociated with a diagnostics model for the first electric machine; andeffecting, based on the first set of diagnostic data, the second set ofdiagnostic data, and the operational data, a change in at least oneparameter associated with a diagnostic unit of the second electricmachine.
 22. The system of claim 21, wherein the operational dataincludes sensor data associated with the first electric machine, andwherein the sensor data includes at least one of voltage, current,temperature, vibration signature, and insulation integrity of the firstelectric machine.
 23. The system of claim 21, wherein the effecting isfurther based on data received by the processor from a remote terminal,the data being associated with an assessment from an operator.
 24. Thesystem of claim 21, wherein the second memory module is configured tocause the processor to perform an adaptive processing of the operationaldata.
 25. The system of claim 21, wherein the first memory module isconfigured to cause the processor to perform a non-adaptive processingof the operational data.
 26. The system of claim 25, wherein thenon-adaptive processing is based on a physics-based model of theelectric machine.
 27. The system of claim 25, wherein the non-adaptiveprocessing is based on a finite-element-analysis (FEA)-based model ofthe electric machine.
 28. The system of claim 25, wherein thenon-adaptive processing is based on a non-learning-based model of theelectric machine.